Self-supervised learning (SSL) has been proposed to alleviate neural networks' reliance on annotated data and to improve downstream tasks' performance, which has obtained substantial success in several volumetric medical image segmentation tasks. However, most existing approaches are designed and pre-trained on CT or MRI datasets of non-brain organs. The lack of brain prior limits those methods' performance on brain segmentation, especially on fine-grained brain parcellation. To overcome this limitation, we here propose a novel SSL strategy for MRI of the human brain, named Masked Deformation Modeling (MDM). MDM first conducts atlas-guided patch sampling on individual brain MRI scans (moving volumes) and an MNI152 template (a fixed volume). The sampled moving volumes are randomly masked in a feature-aligned manner, and then sent into a U-Net-based network to extract latent features. An intensity head and a deformation field head are used to decode the latent features, respectively restoring the masked volume and predicting the deformation field from the moving volume to the fixed volume. The proposed MDM is fine-tuned and evaluated on three brain parcellation datasets with different granularities (JHU, Mindboggle-101, CANDI), a brain lesion segmentation dataset (ATLAS2), and a brain tumor segmentation dataset (BraTS21). Results demonstrate that MDM outperforms various state-of-the-art medical SSL methods by considerable margins, and can effectively reduce the annotation effort by at least 40%. Codes and pre-trained weights will be released at https://github.com/CRazorback/MDM.

Traumatic brain injury (TBI) remains a growing significant public health problem and prognosis of outcome is difficult due to the multitude of factors that underlie the heterogeneity of TBI. Prognosis aims to forecast the likely development of the disease and significantly affects patient's recovery and healthcare. Traditionally, TBI prognosis relies on the physician's insights and their empirical knowledge which makes it infeasible for large-scale implementation. Existing methods utilize a single modality (i.e., either clinical data or Computed Tomography scan images) for TBI prognosis, leaving crucial information from multi-modal data largely underexplored. To address this concern, we explore a Multi-modal Structure-aware Field-wise learning (MSF) method that is capable of mining complex correlations between multi-modal data and TBI outcomes for prognosis on a real-world dataset. Specifically, we develop a High-Level Structure-Aware (HSA) module to capture the structure information of the multilayered clinical data. Experimental results on the publicly available TRACK-TBI dataset demonstrate the viability and effectiveness of our proposed method, by achieving the top-3 accuracy of 96.07% and 98.13% for 3-month and 6-month predictions after injury, respectively.

In this paper, we present the design of a soft wearable exoskeleton that comprises of a glove embedded with pneumatic actuators of variable stiffness for hand assistive and rehabilitation application. The device is lightweight and easily wearable due to the usage of soft pneumatic actuators. A key feature of the device is the variable stiffness of the actuators at different localities that not only conform to the finger profile during actuation, but also provides customizability for different hand dimensions. The actuators can achieve different bending profiles with variable stiffness implemented at different localities. Therefore, the device is able to perform different hand therapy exercises such as full fist, straight fist, hook fist and table top. The device was characterized in terms of its range of motion and maximum force output. Experiments were conducted to examine the differences between active and passive actuation. The results showed that the device could achieve hand grasping and pinching with acceptable range of motion and force.